Face Recognition in Low Resolution Images. Trey Amador Scott Matsumura Matt Yiyang Yan
|
|
- Aron Greene
- 6 years ago
- Views:
Transcription
1 Face Recognition in Low Resolution Images Trey Amador Scott Matsumura Matt Yiyang Yan
2 Introduction
3 Purpose: low resolution facial recognition Extract image/video from source Identify the person in real time given a traineddatabase taken from
4 Face Recognition Libraries histogram of oriented gradients (HOG) dlib Support Vector Machines (SVM)
5 Process Neural Enhance library increase the resolution of low pixel density Theano (neural network) Lasagne (train) upsampled image dlib Histogram of oriented gradients (HOG) SVM feature descriptor for detecting faces
6 Database IMDb Internet Movie Database is an online database of information related to films, television programs and video games low and high resolution versions of the same image highresolution 'base' image to train the Support Vector Machine (SVM)
7 Support Vector Machine for Face Recognition Arnold Schwarzenegger
8 SVM Identify the Rock Image of Images similar to
9 SVM The Rock not The Rock Images similar to Image of
10 SVM Separate data Images similar to Image of
11 SVM Which line? Images similar to Image of
12 SVM Thickest line Images similar to Image of
13 SVM Separate data? Images similar to Image of
14 SVM Nonlinear separation Images similar to Image of
15 Generative Adversarial Network for Upsampling Images
16 GAN Back with The Rock Image of Images similar to
17 GAN Generate this image? Image of Images similar to
18 Generative Network produce an image Discriminative Network real or fake vs
19 How to train your Generative Adversarial Network
20 GAN Train discriminative network real Discriminative Network fake
21 GAN Train both networks random noise Generative Network Discriminative Network Fake negative gradient positive gradient backpropagation
22 GAN Eventually? random noise Generative Network Discriminative Network Real backpropagation
23 GAN Upsampled Generative Network Discriminative Network Real
24 Code can be found at:
25 super resolution video samples
26 face recognition in enhancedresolution video
27 super resolution image enhancement boring Bruce Springsteen 100 x 100 enhanced Bruce Springsteen 200 x 200 actual Bruce Springsteen high res
28 super resolution face recognition unrecognized Bruce Springsteen 100 x 100 that s Bruce Springsteen! 200 x 200
29 experimental paradigm true face high res low res enhanced res false face high res low res enhanced res
30
31
32
33
34 future directions find robust metric with which to filter data test efficacy of various algorithms generate larger dataset
35 References [1] W. Zhao, et al. Face Recognition: A Literature Survey. ACM Computing Surveys, vol. 35, pp , Dec [2] S.C. Park, M.K. Park, and M.G. Kang. SuperResolution Image Reconstruction: A Technical Overview. IEEE Signal Processing Magazine. May [3] D. Glasner, S. Bagon, and M. Irani. SuperResolution from a Single Image, in IEEE 12th ICCV, 2009, pp [4] W.W. Zou and P.C. Yuen. Very Low Resolution Face Recognition Problem. IEEE Transactions on Image Processing, vol. 21, pp , July [5] A. Geitgey, "Face Recognition," GitHub repository, [Online]. Available: [Accessed ]. [6] N. Dalal and B. Triggs. Histogram of Oriented Gradients for Human Detection in CVPR, 2005, pp. 18. [7] P. Felzenszwalb, et al. Object Detection with Discriminantly Trained Part Based Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, pp , Sept [8] C. Cortes and V. Vladimir, "SupportVector Networks," Machine Learning, vol. 20, no. 3, pp , [9] A. J. Champandard, "Neural Enhance," GitHub repository, [Online]. Available: [Accessed ]. [10] D. G. Lowe, "Object Recognition from Local ScaleInvariant Features," Computer Vision, vol. 2, pp , [11] K. Simonyan, M. O. Parkhi, A. Vedaldi and A. Zisserman, "Fisher Vector Faces in the Wild," British Machine Vision Conference, vol. 2, no. 3, p. 4, Sept [12] P. Fischer, A. Dosovitskiy and T. Brox, "Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT," arxiv, p. 10, 22 May [13] M. O. Parkhi, A. Vedaldi and A. Zisserman, "Deep Face Recognition," British Machine Vision Conference, vol. 1, no. 3, p. 6, [14] U. Karn, "An Intuitive Explanation of Convolutional Neural Networks," The Data Science Blog, [Online]. Available: [Accessed ]. [15] C. Ledig, et al. "PhotoRealistic Single Image SuperResolution Using a Generative Adversarial Network," arxiv, p. 19, 25 May [16] A.V. Nefian. Georgia Tech Face Database. Nov. 15, [Online]. Available: [Accessed: Nov. 5, 2017]. [17] Y.D. Wong. ChokePoint Dataset. [Online]. Available: arma.sourceforge.net/chokepoint/. [Accessed: Nov. 5, 2017].
A Neural Algorithm of Artistic Style (2015)
A Neural Algorithm of Artistic Style (2015) Leon A. Gatys, Alexander S. Ecker, Matthias Bethge Nancy Iskander (niskander@dgp.toronto.edu) Overview of Method Content: Global structure. Style: Colours; local
More informationSemantic Localization of Indoor Places. Lukas Kuster
Semantic Localization of Indoor Places Lukas Kuster Motivation GPS for localization [7] 2 Motivation Indoor navigation [8] 3 Motivation Crowd sensing [9] 4 Motivation Targeted Advertisement [10] 5 Motivation
More informationDYNAMIC CONVOLUTIONAL NEURAL NETWORK FOR IMAGE SUPER- RESOLUTION
Journal of Advanced College of Engineering and Management, Vol. 3, 2017 DYNAMIC CONVOLUTIONAL NEURAL NETWORK FOR IMAGE SUPER- RESOLUTION Anil Bhujel 1, Dibakar Raj Pant 2 1 Ministry of Information and
More informationCROSS-LAYER FEATURES IN CONVOLUTIONAL NEURAL NETWORKS FOR GENERIC CLASSIFICATION TASKS. Kuan-Chuan Peng and Tsuhan Chen
CROSS-LAYER FEATURES IN CONVOLUTIONAL NEURAL NETWORKS FOR GENERIC CLASSIFICATION TASKS Kuan-Chuan Peng and Tsuhan Chen Cornell University School of Electrical and Computer Engineering Ithaca, NY 14850
More informationLearning Pixel-Distribution Prior with Wider Convolution for Image Denoising
Learning Pixel-Distribution Prior with Wider Convolution for Image Denoising Peng Liu University of Florida pliu1@ufl.edu Ruogu Fang University of Florida ruogu.fang@bme.ufl.edu arxiv:177.9135v1 [cs.cv]
More informationarxiv: v1 [cs.lg] 2 Jan 2018
Deep Learning for Identifying Potential Conceptual Shifts for Co-creative Drawing arxiv:1801.00723v1 [cs.lg] 2 Jan 2018 Pegah Karimi pkarimi@uncc.edu Kazjon Grace The University of Sydney Sydney, NSW 2006
More informationDeep filter banks for texture recognition and segmentation
Deep filter banks for texture recognition and segmentation Mircea Cimpoi, University of Oxford Subhransu Maji, UMASS Amherst Andrea Vedaldi, University of Oxford Texture understanding 2 Indicator of materials
More informationStudy Impact of Architectural Style and Partial View on Landmark Recognition
Study Impact of Architectural Style and Partial View on Landmark Recognition Ying Chen smileyc@stanford.edu 1. Introduction Landmark recognition in image processing is one of the important object recognition
More informationColorful Image Colorizations Supplementary Material
Colorful Image Colorizations Supplementary Material Richard Zhang, Phillip Isola, Alexei A. Efros {rich.zhang, isola, efros}@eecs.berkeley.edu University of California, Berkeley 1 Overview This document
More informationAutocomplete Sketch Tool
Autocomplete Sketch Tool Sam Seifert, Georgia Institute of Technology Advanced Computer Vision Spring 2016 I. ABSTRACT This work details an application that can be used for sketch auto-completion. Sketch
More informationAn Efficient Approach to Face Recognition Using a Modified Center-Symmetric Local Binary Pattern (MCS-LBP)
, pp.13-22 http://dx.doi.org/10.14257/ijmue.2015.10.8.02 An Efficient Approach to Face Recognition Using a Modified Center-Symmetric Local Binary Pattern (MCS-LBP) Anusha Alapati 1 and Dae-Seong Kang 1
More informationtsushi Sasaki Fig. Flow diagram of panel structure recognition by specifying peripheral regions of each component in rectangles, and 3 types of detect
RECOGNITION OF NEL STRUCTURE IN COMIC IMGES USING FSTER R-CNN Hideaki Yanagisawa Hiroshi Watanabe Graduate School of Fundamental Science and Engineering, Waseda University BSTRCT For efficient e-comics
More informationInternational Journal of Modern Trends in Engineering and Research e-issn No.: , Date: 2-4 July, 2015
International Journal of Modern Trends in Engineering and Research www.ijmter.com e-issn No.:2349-9745, Date: 2-4 July, 2015 Illumination Invariant Face Recognition Sailee Salkar 1, Kailash Sharma 2, Nikhil
More informationTRANSFORMING PHOTOS TO COMICS USING CONVOLUTIONAL NEURAL NETWORKS. Tsinghua University, China Cardiff University, UK
TRANSFORMING PHOTOS TO COMICS USING CONVOUTIONA NEURA NETWORKS Yang Chen Yu-Kun ai Yong-Jin iu Tsinghua University, China Cardiff University, UK ABSTRACT In this paper, inspired by Gatys s recent work,
More informationCombination of Single Image Super Resolution and Digital Inpainting Algorithms Based on GANs for Robust Image Completion
SERBIAN JOURNAL OF ELECTRICAL ENGINEERING Vol. 14, No. 3, October 2017, 379-386 UDC: 004.932.4+004.934.72 DOI: https://doi.org/10.2298/sjee1703379h Combination of Single Image Super Resolution and Digital
More informationSupplementary Material: Deep Photo Enhancer: Unpaired Learning for Image Enhancement from Photographs with GANs
Supplementary Material: Deep Photo Enhancer: Unpaired Learning for Image Enhancement from Photographs with GANs Yu-Sheng Chen Yu-Ching Wang Man-Hsin Kao Yung-Yu Chuang National Taiwan University 1 More
More informationIn-Vehicle Hand Gesture Recognition using Hidden Markov Models
2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) Windsor Oceanico Hotel, Rio de Janeiro, Brazil, November 1-4, 2016 In-Vehicle Hand Gesture Recognition using Hidden
More informationDetection of License Plate using Sliding Window, Histogram of Oriented Gradient, and Support Vector Machines Method
Journal of Physics: Conference Series PAPER OPEN ACCESS Detection of License Plate using Sliding Window, Histogram of Oriented Gradient, and Support Vector Machines Method To cite this article: INGA Astawa
More informationA TWO-PART PREDICTIVE CODER FOR MULTITASK SIGNAL COMPRESSION. Scott Deeann Chen and Pierre Moulin
A TWO-PART PREDICTIVE CODER FOR MULTITASK SIGNAL COMPRESSION Scott Deeann Chen and Pierre Moulin University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering 5 North Mathews
More informationFace detection, face alignment, and face image parsing
Lecture overview Face detection, face alignment, and face image parsing Brandon M. Smith Guest Lecturer, CS 534 Monday, October 21, 2013 Brief introduction to local features Face detection Face alignment
More informationList of Publications for Thesis
List of Publications for Thesis Felix Juefei-Xu CyLab Biometrics Center, Electrical and Computer Engineering Carnegie Mellon University, Pittsburgh, PA 15213, USA felixu@cmu.edu 1. Journal Publications
More informationConvolutional Neural Network-Based Infrared Image Super Resolution Under Low Light Environment
Convolutional Neural Network-Based Infrared Super Resolution Under Low Light Environment Tae Young Han, Yong Jun Kim, Byung Cheol Song Department of Electronic Engineering Inha University Incheon, Republic
More informationCOMP 776 Computer Vision Project Final Report Distinguishing cartoon image and paintings from photographs
COMP 776 Computer Vision Project Final Report Distinguishing cartoon image and paintings from photographs Sang Woo Lee 1. Introduction With overwhelming large scale images on the web, we need to classify
More informationEvolutionary Learning of Local Descriptor Operators for Object Recognition
Genetic and Evolutionary Computation Conference Montréal, Canada 6th ANNUAL HUMIES AWARDS Evolutionary Learning of Local Descriptor Operators for Object Recognition Present : Cynthia B. Pérez and Gustavo
More informationMultiple Kernels for Object Detection. Andrea Vedaldi Varun Gulshan Manik Varma Andrew Zisserman
Multiple Kernels for Object Detection Andrea Vedaldi Varun Gulshan Manik Varma Andrew Zisserman MK classification PHOW Gray MK SVM PHOW Color combine one kernel per histogram PHOG PHOG Sym Feature vector
More informationCS688/WST665 Student presentation Learning Fine-grained Image Similarity with Deep Ranking CVPR Gayoung Lee ( 이가영 )
CS688/WST665 Student presentation Learning Fine-grained Image Similarity with Deep Ranking CVPR 2014 Gayoung Lee ( 이가영 ) Contents 1. Background knowledge 2. Proposed method 3. Experimental Result 4. Conclusion
More informationFrom Reality to Perception: Genre-Based Neural Image Style Transfer
From Reality to Perception: Genre-Based Neural Image Style Transfer Zhuoqi Ma, Nannan Wang, Xinbo Gao, Jie Li State Key Laboratory of Integrated Services Networks, School of Electronic Engineering, Xidian
More informationVisualizing and Understanding. Fei-Fei Li & Justin Johnson & Serena Yeung. Lecture 12 -
Lecture 12: Visualizing and Understanding Lecture 12-1 May 16, 2017 Administrative Milestones due tonight on Canvas, 11:59pm Midterm grades released on Gradescope this week A3 due next Friday, 5/26 HyperQuest
More informationBiologically Inspired Computation
Biologically Inspired Computation Deep Learning & Convolutional Neural Networks Joe Marino biologically inspired computation biological intelligence flexible capable of detecting/ executing/reasoning about
More informationComparing Computer-predicted Fixations to Human Gaze
Comparing Computer-predicted Fixations to Human Gaze Yanxiang Wu School of Computing Clemson University yanxiaw@clemson.edu Andrew T Duchowski School of Computing Clemson University andrewd@cs.clemson.edu
More informationAn Analysis on Visual Recognizability of Onomatopoeia Using Web Images and DCNN features
An Analysis on Visual Recognizability of Onomatopoeia Using Web Images and DCNN features Wataru Shimoda Keiji Yanai Department of Informatics, The University of Electro-Communications 1-5-1 Chofugaoka,
More informationLive Hand Gesture Recognition using an Android Device
Live Hand Gesture Recognition using an Android Device Mr. Yogesh B. Dongare Department of Computer Engineering. G.H.Raisoni College of Engineering and Management, Ahmednagar. Email- yogesh.dongare05@gmail.com
More informationAn Un-awarely Collected Real World Face Database: The ISL-Door Face Database
An Un-awarely Collected Real World Face Database: The ISL-Door Face Database Hazım Kemal Ekenel, Rainer Stiefelhagen Interactive Systems Labs (ISL), Universität Karlsruhe (TH), Am Fasanengarten 5, 76131
More informationarxiv: v1 [cs.cv] 19 Apr 2018
Survey of Face Detection on Low-quality Images arxiv:1804.07362v1 [cs.cv] 19 Apr 2018 Yuqian Zhou, Ding Liu, Thomas Huang Beckmann Institute, University of Illinois at Urbana-Champaign, USA {yuqian2, dingliu2}@illinois.edu
More informationSuggested projects for EL-GY 6123 Image and Video Processing (Spring 2018) 360 Degree Video View Prediction (contact: Chenge Li,
Suggested projects for EL-GY 6123 Image and Video Processing (Spring 2018) Updated 2/6/2018 360 Degree Video View Prediction (contact: Chenge Li, cl2840@nyu.edu) Pan, Junting, et al. "Shallow and deep
More informationEffective Pixel Interpolation for Image Super Resolution
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-iss: 2278-2834,p- ISS: 2278-8735. Volume 6, Issue 2 (May. - Jun. 2013), PP 15-20 Effective Pixel Interpolation for Image Super Resolution
More informationResearch on Hand Gesture Recognition Using Convolutional Neural Network
Research on Hand Gesture Recognition Using Convolutional Neural Network Tian Zhaoyang a, Cheng Lee Lung b a Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China E-mail address:
More informationDerek Allman a, Austin Reiter b, and Muyinatu Bell a,c
Exploring the effects of transducer models when training convolutional neural networks to eliminate reflection artifacts in experimental photoacoustic images Derek Allman a, Austin Reiter b, and Muyinatu
More informationEffects of the Unscented Kalman Filter Process for High Performance Face Detector
Effects of the Unscented Kalman Filter Process for High Performance Face Detector Bikash Lamsal and Naofumi Matsumoto Abstract This paper concerns with a high performance algorithm for human face detection
More informationImage Forgery Detection Using Svm Classifier
Image Forgery Detection Using Svm Classifier Anita Sahani 1, K.Srilatha 2 M.E. Student [Embedded System], Dept. Of E.C.E., Sathyabama University, Chennai, India 1 Assistant Professor, Dept. Of E.C.E, Sathyabama
More informationDetection and Segmentation. Fei-Fei Li & Justin Johnson & Serena Yeung. Lecture 11 -
Lecture 11: Detection and Segmentation Lecture 11-1 May 10, 2017 Administrative Midterms being graded Please don t discuss midterms until next week - some students not yet taken A2 being graded Project
More informationBook Cover Recognition Project
Book Cover Recognition Project Carolina Galleguillos Department of Computer Science University of California San Diego La Jolla, CA 92093-0404 cgallegu@cs.ucsd.edu Abstract The purpose of this project
More informationClassification of Road Images for Lane Detection
Classification of Road Images for Lane Detection Mingyu Kim minkyu89@stanford.edu Insun Jang insunj@stanford.edu Eunmo Yang eyang89@stanford.edu 1. Introduction In the research on autonomous car, it is
More informationAutomatic Image Cropping and Selection using Saliency: an Application to Historical Manuscripts
Automatic Image Cropping and Selection using Saliency: an Application to Historical Manuscripts Marcella Cornia, Stefano Pini, Lorenzo Baraldi, and Rita Cucchiara University of Modena and Reggio Emilia
More informationAnalysis of adversarial attacks against CNN-based image forgery detectors
Analysis of adversarial attacks against CNN-based image forgery detectors Diego Gragnaniello, Francesco Marra, Giovanni Poggi, Luisa Verdoliva Department of Electrical Engineering and Information Technology
More informationSuper resolution with Epitomes
Super resolution with Epitomes Aaron Brown University of Wisconsin Madison, WI Abstract Techniques exist for aligning and stitching photos of a scene and for interpolating image data to generate higher
More informationChess Recognition Using Computer Vision
Chess Recognition Using Computer Vision May 30, 2017 Ramani Varun (U6004067, contribution 50%) Sukrit Gupta (U5900600, contribution 50%) College of Engineering & Computer Science he Australian National
More informationarxiv: v3 [cs.cv] 18 Dec 2018
Video Colorization using CNNs and Keyframes extraction: An application in saving bandwidth Ankur Singh 1 Anurag Chanani 2 Harish Karnick 3 arxiv:1812.03858v3 [cs.cv] 18 Dec 2018 Abstract In this paper,
More informationEFFECTS OF SEVERE SIGNAL DEGRADATION ON EAR DETECTION. J. Wagner, A. Pflug, C. Rathgeb and C. Busch
EFFECTS OF SEVERE SIGNAL DEGRADATION ON EAR DETECTION J. Wagner, A. Pflug, C. Rathgeb and C. Busch da/sec Biometrics and Internet Security Research Group Hochschule Darmstadt, Darmstadt, Germany {johannes.wagner,anika.pflug,christian.rathgeb,christoph.busch}@cased.de
More informationSMART SURVEILLANCE SYSTEM FOR FACE RECOGNITION
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 8, August 2014,
More informationAnalyzing features learned for Offline Signature Verification using Deep CNNs
Accepted as a conference paper for ICPR 2016 Analyzing features learned for Offline Signature Verification using Deep CNNs Luiz G. Hafemann, Robert Sabourin Lab. d imagerie, de vision et d intelligence
More informationImproved SIFT Matching for Image Pairs with a Scale Difference
Improved SIFT Matching for Image Pairs with a Scale Difference Y. Bastanlar, A. Temizel and Y. Yardımcı Informatics Institute, Middle East Technical University, Ankara, 06531, Turkey Published in IET Electronics,
More informationSynthetic View Generation for Absolute Pose Regression and Image Synthesis: Supplementary material
Synthetic View Generation for Absolute Pose Regression and Image Synthesis: Supplementary material Pulak Purkait 1 pulak.cv@gmail.com Cheng Zhao 2 irobotcheng@gmail.com Christopher Zach 1 christopher.m.zach@gmail.com
More informationMulti-task Learning of Dish Detection and Calorie Estimation
Multi-task Learning of Dish Detection and Calorie Estimation Department of Informatics, The University of Electro-Communications, Tokyo 1-5-1 Chofugaoka, Chofu-shi, Tokyo 182-8585 JAPAN ABSTRACT In recent
More informationAVA: A Large-Scale Database for Aesthetic Visual Analysis
1 AVA: A Large-Scale Database for Aesthetic Visual Analysis Wei-Ta Chu National Chung Cheng University N. Murray, L. Marchesotti, and F. Perronnin, AVA: A Large-Scale Database for Aesthetic Visual Analysis,
More informationAnalysis of Various Methodology of Hand Gesture Recognition System using MATLAB
Analysis of Various Methodology of Hand Gesture Recognition System using MATLAB Komal Hasija 1, Rajani Mehta 2 Abstract Recognition is a very effective area of research in regard of security with the involvement
More informationSemantic Segmentation in Red Relief Image Map by UX-Net
Semantic Segmentation in Red Relief Image Map by UX-Net Tomoya Komiyama 1, Kazuhiro Hotta 1, Kazuo Oda 2, Satomi Kakuta 2 and Mikako Sano 2 1 Meijo University, Shiogamaguchi, 468-0073, Nagoya, Japan 2
More informationReal Time Face Recognition using Raspberry Pi II
Real Time Face Recognition using Raspberry Pi II A.Viji 1, A.Pavithra 2 Department of Electronics Engineering, Madras Institute of Technology, Anna University, Chennai, India 1 Department of Electronics
More informationNU-Net: Deep Residual Wide Field of View Convolutional Neural Network for Semantic Segmentation
NU-Net: Deep Residual Wide Field of View Convolutional Neural Network for Semantic Segmentation Mohamed Samy 1 Karim Amer 1 Kareem Eissa Mahmoud Shaker Mohamed ElHelw Center for Informatics Science Nile
More informationPrivacy-Protected Camera for the Sensing Web
Privacy-Protected Camera for the Sensing Web Ikuhisa Mitsugami 1, Masayuki Mukunoki 2, Yasutomo Kawanishi 2, Hironori Hattori 2, and Michihiko Minoh 2 1 Osaka University, 8-1, Mihogaoka, Ibaraki, Osaka
More informationDeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition. ECE 289G: Paper Presentation #3 Philipp Gysel
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition ECE 289G: Paper Presentation #3 Philipp Gysel Autonomous Car ECE 289G Paper Presentation, Philipp Gysel Slide 2 Source: maps.google.com
More informationEnhancing Symmetry in GAN Generated Fashion Images
Enhancing Symmetry in GAN Generated Fashion Images Vishnu Makkapati 1 and Arun Patro 2 1 Myntra Designs Pvt. Ltd., Bengaluru - 560068, India vishnu.makkapati@myntra.com 2 Department of Electrical Engineering,
More informationPassionate about building the next generation of computer vision and machine learning technology.
Pro Vision Lab Passionate about building the next generation of computer vision and machine learning technology. We develop turnkey solutions and innovative applications involving object detection/tracking
More informationColor Constancy Using Standard Deviation of Color Channels
2010 International Conference on Pattern Recognition Color Constancy Using Standard Deviation of Color Channels Anustup Choudhury and Gérard Medioni Department of Computer Science University of Southern
More informationMulti-Modal Spectral Image Super-Resolution
Multi-Modal Spectral Image Super-Resolution Fayez Lahoud, Ruofan Zhou, and Sabine Süsstrunk School of Computer and Communication Sciences École Polytechnique Fédérale de Lausanne {ruofan.zhou,fayez.lahoud,sabine.susstrunk}@epfl.ch
More informationWadehra Kartik, Kathpalia Mukul, Bahl Vasudha, International Journal of Advance Research, Ideas and Innovations in Technology
ISSN: 2454-132X Impact factor: 4.295 (Volume 4, Issue 1) Available online at www.ijariit.com Hand Detection and Gesture Recognition in Real-Time Using Haar-Classification and Convolutional Neural Networks
More informationA Survey on Different Face Detection Algorithms in Image Processing
A Survey on Different Face Detection Algorithms in Image Processing Doyle Fermi 1, Faiza N B 2, Ranjana Radhakrishnan 3, Swathi S Kartha 4, Anjali S 5 U.G. Student, Department of Computer Engineering,
More informationDevelopment of Hybrid Image Sensor for Pedestrian Detection
AUTOMOTIVE Development of Hybrid Image Sensor for Pedestrian Detection Hiroaki Saito*, Kenichi HatanaKa and toshikatsu HayaSaKi To reduce traffic accidents and serious injuries at intersections, development
More informationAttack-Resistant aicaptcha using a Negative Selection Artificial Immune System
2017 IEEE Symposium on Security and Privacy Workshops Attack-Resistant aicaptcha using a Negative Selection Artificial Immune System Brian M. Powell 1, Ekampreet Kalsy 2, Gaurav Goswami 2, Mayank Vatsa
More informationProject Title: Sparse Image Reconstruction with Trainable Image priors
Project Title: Sparse Image Reconstruction with Trainable Image priors Project Supervisor(s) and affiliation(s): Stamatis Lefkimmiatis, Skolkovo Institute of Science and Technology (Email: s.lefkimmiatis@skoltech.ru)
More informationSketch-a-Net that Beats Humans
Sketch-a-Net that Beats Humans Qian Yu SketchLab@QMUL Queen Mary University of London 1 Authors Qian Yu Yongxin Yang Yi-Zhe Song Tao Xiang Timothy Hospedales 2 Let s play a game! Round 1 Easy fish face
More informationIMAGE RESTORATION WITH NEURAL NETWORKS. Orazio Gallo Work with Hang Zhao, Iuri Frosio, Jan Kautz
IMAGE RESTORATION WITH NEURAL NETWORKS Orazio Gallo Work with Hang Zhao, Iuri Frosio, Jan Kautz MOTIVATION The long path of images Bad Pixel Correction Black Level AF/AE Demosaic Denoise Lens Correction
More informationNikhil Gupta *1, Dr Rakesh Dhiman 2 ABSTRACT I. INTRODUCTION
International Journal of Scientific Research in Computer Science, Engineering and Information Technology 2017 IJSRCSEIT Volume 2 Issue 6 ISSN : 2456-3307 An Offline Handwritten Signature Verification Using
More informationCOLOR FEATURES FOR DATING HISTORICAL COLOR IMAGES
COLOR FEATURES FOR DATING HISTORICAL COLOR IMAGES Basura Fernando, Damien Muselet, Rahat Khan and Tinne Tuytelaars PSI-VISICS, KU Leuven, iminds, Belgium Universit Jean Monnet, LaHC, Saint-Etienne, France
More informationHuman-Centric Trusted AI for Data-Driven Economy
Human-Centric Trusted AI for Data-Driven Economy Masugi Inoue 1 and Hideyuki Tokuda 2 National Institute of Information and Communications Technology inoue@nict.go.jp 1, Director, International Research
More informationNon-Uniform Motion Blur For Face Recognition
IOSR Journal of Engineering (IOSRJEN) ISSN (e): 2250-3021, ISSN (p): 2278-8719 Vol. 08, Issue 6 (June. 2018), V (IV) PP 46-52 www.iosrjen.org Non-Uniform Motion Blur For Face Recognition Durga Bhavani
More informationCS231A Final Project: Who Drew It? Style Analysis on DeviantART
CS231A Final Project: Who Drew It? Style Analysis on DeviantART Mindy Huang (mindyh) Ben-han Sung (bsung93) Abstract Our project studied popular portrait artists on Deviant Art and attempted to identify
More informationAn Image Processing Based Pedestrian Detection System for Driver Assistance
I J C T A, 9(15), 2016, pp. 7369-7375 International Science Press An Image Processing Based Pedestrian Detection System for Driver Assistance Sandeep A. K.*, Nithin S.** and K. I. Ramachandran*** ABSTRACT
More informationLabVIEW based Intelligent Frontal & Non- Frontal Face Recognition System
LabVIEW based Intelligent Frontal & Non- Frontal Face Recognition System Muralindran Mariappan, Manimehala Nadarajan, and Karthigayan Muthukaruppan Abstract Face identification and tracking has taken a
More informationMSR Asia MSM at ActivityNet Challenge 2017: Trimmed Action Recognition, Temporal Action Proposals and Dense-Captioning Events in Videos
MSR Asia MSM at ActivityNet Challenge 2017: Trimmed Action Recognition, Temporal Action Proposals and Dense-Captioning Events in Videos Ting Yao, Yehao Li, Zhaofan Qiu, Fuchen Long, Yingwei Pan, Dong Li,
More informationSIMULATION-BASED MODEL CONTROL USING STATIC HAND GESTURES IN MATLAB
SIMULATION-BASED MODEL CONTROL USING STATIC HAND GESTURES IN MATLAB S. Kajan, J. Goga Institute of Robotics and Cybernetics, Faculty of Electrical Engineering and Information Technology, Slovak University
More informationHand Gesture Recognition by Means of Region- Based Convolutional Neural Networks
Contemporary Engineering Sciences, Vol. 10, 2017, no. 27, 1329-1342 HIKARI Ltd, www.m-hikari.com https://doi.org/10.12988/ces.2017.710154 Hand Gesture Recognition by Means of Region- Based Convolutional
More informationImproved Detection of LSB Steganography in Grayscale Images
Improved Detection of LSB Steganography in Grayscale Images Andrew Ker adk@comlab.ox.ac.uk Royal Society University Research Fellow at Oxford University Computing Laboratory Information Hiding Workshop
More informationA Single Image Haze Removal Algorithm Using Color Attenuation Prior
International Journal of Scientific and Research Publications, Volume 6, Issue 6, June 2016 291 A Single Image Haze Removal Algorithm Using Color Attenuation Prior Manjunath.V *, Revanasiddappa Phatate
More informationReal Time Multimodal Emotion Recognition System using Facial Landmarks and Hand over Face Gestures
Real Time Multimodal Emotion Recognition System using Facial Landmarks and Hand over Face Gestures Mahesh Krishnananda Prabhu and Dinesh Babu Jayagopi Abstract Over the last few years, emotional intelligent
More informationDeep Learning for Human Activity Recognition: A Resource Efficient Implementation on Low-Power Devices
Deep Learning for Human Activity Recognition: A Resource Efficient Implementation on Low-Power Devices Daniele Ravì, Charence Wong, Benny Lo and Guang-Zhong Yang To appear in the proceedings of the IEEE
More informationVEHICLE LICENSE PLATE DETECTION ALGORITHM BASED ON STATISTICAL CHARACTERISTICS IN HSI COLOR MODEL
VEHICLE LICENSE PLATE DETECTION ALGORITHM BASED ON STATISTICAL CHARACTERISTICS IN HSI COLOR MODEL Instructor : Dr. K. R. Rao Presented by: Prasanna Venkatesh Palani (1000660520) prasannaven.palani@mavs.uta.edu
More informationNew Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution
New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution Yijie Bei Alex Damian Shijia Hu Sachit Menon Nikhil Ravi Cynthia Rudin Duke University
More informationMARCO PEDERSOLI. Assistant Professor at ETS Montreal profs.etsmtl.ca/mpedersoli
MARCO PEDERSOLI Assistant Professor at ETS Montreal profs.etsmtl.ca/mpedersoli RESEARCH INTERESTS Visual Recognition, Efficient Deep Learning, Learning with Reduced Supervision, Data Exploration ACADEMIC
More informationSketch Matching for Crime Investigation using LFDA Framework
International Journal of Engineering and Technical Research (IJETR) Sketch Matching for Crime Investigation using LFDA Framework Anjali J. Pansare, Dr.V.C.Kotak, Babychen K. Mathew Abstract Here we are
More informationToward Automatic and Objective Evaluation of Synchronization in Synchronized Diving Video
https://doi.org/10.2352/issn.2470-1173.2018.2.vipc-205 2018, Society for Imaging Science and Technology Toward Automatic and Objective Evaluation of Synchronization in Synchronized Diving Video Yixin Du
More informationJoint Demosaicing and Super-Resolution Imaging from a Set of Unregistered Aliased Images
Joint Demosaicing and Super-Resolution Imaging from a Set of Unregistered Aliased Images Patrick Vandewalle a, Karim Krichane a, David Alleysson b, and Sabine Süsstrunk a a School of Computer and Communication
More informationContinuous Gesture Recognition Fact Sheet
Continuous Gesture Recognition Fact Sheet August 17, 2016 1 Team details Team name: ICT NHCI Team leader name: Xiujuan Chai Team leader address, phone number and email Address: No.6 Kexueyuan South Road
More informationMachine Learning. Classification, Discriminative learning. Marc Toussaint University of Stuttgart Summer 2014
Machine Learning Classification, Discriminative learning Structured output, structured input, discriminative function, joint input-output features, Likelihood Maximization, Logistic regression, binary
More informationA Novel Algorithm for Hand Vein Recognition Based on Wavelet Decomposition and Mean Absolute Deviation
Sensors & Transducers, Vol. 6, Issue 2, December 203, pp. 53-58 Sensors & Transducers 203 by IFSA http://www.sensorsportal.com A Novel Algorithm for Hand Vein Recognition Based on Wavelet Decomposition
More informationLANDMARK recognition is an important feature for
1 NU-LiteNet: Mobile Landmark Recognition using Convolutional Neural Networks Chakkrit Termritthikun, Surachet Kanprachar, Paisarn Muneesawang arxiv:1810.01074v1 [cs.cv] 2 Oct 2018 Abstract The growth
More informationLEARNING AN INVERSE TONE MAPPING NETWORK WITH A GENERATIVE ADVERSARIAL REGULARIZER
LEARNING AN INVERSE TONE MAPPING NETWORK WITH A GENERATIVE ADVERSARIAL REGULARIZER Shiyu Ning, Hongteng Xu,3, Li Song, Rong Xie, Wenjun Zhang School of Electronic Information and Electrical Engineering,
More informationAutomated hand recognition as a human-computer interface
Automated hand recognition as a human-computer interface Sergii Shelpuk SoftServe, Inc. sergii.shelpuk@gmail.com Abstract This paper investigates applying Machine Learning to the problem of turning a regular
More informationColour correction for panoramic imaging
Colour correction for panoramic imaging Gui Yun Tian Duke Gledhill Dave Taylor The University of Huddersfield David Clarke Rotography Ltd Abstract: This paper reports the problem of colour distortion in
More informationAdaptive Vision Leveraging Digital Retinas: Extracting Meaningful Segments
Adaptive Vision Leveraging Digital Retinas: Extracting Meaningful Segments Nicolas Burrus and Thierry M Bernard September 20, 2006 Nicolas Burrus Adaptive Vision Leveraging
More information